Informatics Publishing Limited

S. Santhosh Baboo ¹, V. J. Chakravarthy ²

Affiliations
1 Postgraduate and Research Department of Computer Science, Dwaraka Doss Goverdhan Doss Vaishnav College, Chennai, IN
2 PG Department of Computer Science, The New College, Chennai, IN

Abstract

In this paper, we address the usage of cross-layer networking, where the physical and MAC layer knowledge of the wireless medium is shared with higher layers, in order to provide efficient methods of allocating networks resources and applications over the Internet. Cross-layer design breaks away from traditional network design where each layer of the protocol stack operates independently. In this new approach information is exchanged between different layers of the protocol stack, and end-to-end performance is optimized by adapting to this information at each protocol layer. With the above cross-layer design, we incorporated the other model called, video distortion model used for analyzing the performance of multipath routing for low latency video streaming, in bandwidth-limited ad hoc networks. The video distortion model which captures the influence of both the encoder performance and network congestion. In this type of environments, a single node transmitting multimedia data may have an impact on the overall network conditions and may need to limits its rate to achieve the highest sustainable video quality.

Keywords

Cross lAyer Design - Ad Hoc Networks - Congestion - PSNR – GOP- Routing Protocol.

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S. Santhosh Baboo , M. Renuka Devi ¹

Affiliations
1 Department of MCA at Sree Saraswathi Thyagaraja College, IN

Abstract

Compression is used just about everywhere. Themotivation of the compression is reduction of data storage and transmission bandwidth requirements. Compression helps reduce the consumption of expensive resources, such as hard disk space or transmission bandwidth. So the growth of volume of data has led to a need for “Data Compression “, that is the ability to reduce the amount of storage or Internet bandwidth required to handle this data. Compression is the reduction in size of data in order to save space or transmission time. For data transmission, compression can be performed on just the data content or on the entire transmission unit depending on a number of factors. This paper summarizes the different issues related with the various compression algorithms such as Huffman Coding, LZW Coding, Arithmetic Coding, BWT related with various authors. By implementing these algorithms, I get some results and regarding to these results we suggest the efficient algorithm to be used with a certain type of file to be compressed. This paper compares the features of these algorithms also such as Compression speed, Decompression speed, Memory space occupied, Compression ratio etc.

Keywords

HUFFMAN, LZW, BWT.

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S. Santhosh Baboo ¹, A. Malathi ²

Affiliations
1 Department of Computer Science, Dwaraka Doss Goverdhan Doss Vaishnav College (Accredited at ‘A’ Grade by NAAC), Chennai, Tamil Nadu, IN
2 Department of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu, IN

Abstract

Crime data mining has gained popularity due to the increase in volume of crime data along with the incidence and complexity of crimes. We use data mining methods for analyzing city crime data. The result of this data mining could potentially be used to lesion and even prevent the crimes. A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. This paper proposes a crime data mining system to analyze the city crime data, to preprocess the data set and predict the population using clustering.

Keywords

Crime Data Mining, Clustering and Pre Processing.

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S. Santhosh Baboo ¹, I. Kadar Shereef ²

Affiliations
1 Postgraduate and Research, Department of Computer Science at Dwaraka Doss Goverdhan Doss Vaishnav College, IN
2 Sree Saraswathi Thyagaraja College of Arts and Science, Pollachi, IN

Abstract

In this paper, primarily hybrid network is illustrated, which integrates a Self-Organizing Feature Map (SOFM) and a Multilayer Perceptron Network (MLP) to understand a much better prediction system. Then, it is demonstrated that the use of appropriate features can not only reduce the number of features, but also can improve the prediction accuracy. A feature selection MLP selects significant features online while learning the prediction task. Moreover, in this proposed approach, MLP is trained using Modified Levenberg-Marquardt algorithm for better convergence and performance. The experimental results show that the proposed approach provides significant prediction result with very less error rates.

Keywords

SOFM, MLP, Modified Levenberg-Marquardt, Prediction.

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H. Sivagami ¹, S. Santhosh Baboo ¹

Affiliations
1 Research Department of Computer Science, D.G.Vaishnav College, Arumbakkam, Chennai-600106, Tamil Nadu, IN

Abstract

Objective: The main goal of this paper is to enhance fusion of sonar image thereby achieve the better entropy, standard deviation and PSNR value. Methods: Multi-Scale Transforms (MST) and Sparse Representation (SR) methods are the two well-known methods used in image and signal representation theory. The novel image fusion framework is proposed in this paper by the combining enhanced MST method and SR based image fusion. The proposed scheme consists of three phase; first de-noised the sonar image using DTCWT with mean filter; second select the obtained pixels or features from sonar image using Novel PCA method; third obtained fusion image using Enhanced MST with SR structure. Findings: It is good at suppressing noise, especially for images with a higher noise level. The advantage of the proposed enhanced MST with SR technique than conventional MST with SR method is different level of decomposition using four popular MST methods; DWT, DTCWT, CVT and NSCT. The proposed method obtained better result in terms of entropy, standard deviation compared to conventional method. Applications: To realize earth surfaces with focus on underwater applications like depth sounding, sea-bed imaging and fish echolocation the SOund Navigation And Ranging (SONAR) technology is used.

Keywords

Denoising, Image Fusion, Multi-Scale Transforms, Sparse Representation, Sonar Image.

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